Ensuring high On-Shelf Availability (OSA) is essential for retailers today. It is a measure of retailer performance. Out-of Stock is a major problem in retailing, as it leads to lost sales and decreased consumer loyalty. The term "Out-of-Stock" is used to describe a situation where a consumer does not find the product on the shelf, at the time he/she wishes to purchase it. The root causes leading to OOS include inventory inaccuracy, unexpected high demand, restock frequency and poor shelf monitoring.Yet, the possibilities for detecting and measuring an out-of shelf situation are limited, mainly involving visual shelf audits. Hence, the existence of an automatic method for detecting the products that are not on the shelf, would be valuable, offering an accurate view of the shelf availability to the store manager. The proposed solution, is a real-time application connected to a camera device that monitors the onshelf availability of products and sends alerts to the store manager when products go out of shelf or are misplaced. The proposed solution, compared to the existing solutions, is cost effective, easy to implement and easy to use.
Object tracking is a noteworthy application in the field of wireless sensor networks that has attracted major Research attention recently. Most object tracking schemes uses prediction scheme to minimize the energy consumption and to maintain low missing rate in a sensor network. However objects need to be localize, when object was found missing during tracking process. In this article, we proposed a swarm intelligence mechanism, such as particle swarm optimization (PSO) to accurately estimate the location of the missing object, using updated object position and velocity and the extensive simulations are also shown to demonstrate the effectiveness of the proposed algorithm against the centroid and weighted centroid methods to evaluate its performance in terms of localization error.
Pattern classification is of significant demand in the field of machine learning. Its applications range from a simple problem of speech recognition to a complex and important problem of medical diagnosis. Fuzzy based algorithms have been one of the most important methods which have contributed in solving the pattern classification problem. A customized Fuzzy based Supervised Hypersphere Neural Network (SHNN) is presented for the use of pattern classification. Here, a modernized pattern classification method has been presented by considering the fuzzy hypersphere neural network concept at the back end and using a modified version of the membership function aiming to solve the pattern classification problems and boost the performance of the algorithm. The proposed SHNN model creates supervised hypersphere using measurements obtained from intra-class distance techniques along with individual class pattern choice, over the fuzzy membership function. The previous modified fuzzy approaches presented an inherent drawback of ambiguous assignment of classes, losing the fuzzy nature during the assigning of classes in the testing phase and over fitting of the model during the training phase. The proposed approach solves this problem by adding a non linearity to the output of the membership function to maintain its fuzzy nature. Additionally, a new weighted Euclidean distance equation has been designed to enhance the performance of the algorithm. The performance of the proposed model of SHNN has been examined on four standard datasets namely - Pima, liver, glass and monks-3. The results obtained were superior to the previous proposed approaches. Thus, presenting a new state result of the art on the datasets.
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